Materials Today: Proceedings xxx (xxxx) xxx

Contents lists available at ScienceDirect

Materials Today: Proceedings

journal homepage: www.elsevier.com/locate/matpr

A new algorithm for detection of nodes failures and enhancement of

network coverage and energy usage in wireless sensor networks
P. Satyanarayana a,⇑, T. Mahalakshmi b, R .Sivakami c, Saad Ali Alahmari d, Sivaram Rajeyyagari d,
Srinivasulu Asadi e
a
ECE, V. R. Siddhartha Engineering College, Vijayawada, India
b
Department of ECE, Prasad V Potluri Siddhartha Institute of Technology, India
c
Associate Professor,CSE, Sona College of Technology, Salem 5, TN, India
d
Department of Computer Science, Shaqra University, Saudi Arabia
e
Data Analytics Research Laboratory, BlueCrest University College, Monrovia, Liberia

a r t i c l e i n f o a b s t r a c t

Article history: Present natural lives shows rising attention in various applications of the wireless sensor networks
Available online xxxx (WSNs). Significant applications are isolated and stiff regions where human involvement is hazardous
or unfeasible. Some examples are space investigation, battlefield observation, costal, and edge security;
Keywords: nowadays, such networks are needful in several manufacturing and customer applications. A WSN is a
Wireless sensor network device system, indicated as nodes; these nodes intellect the atmosphere and transfer the details collected
Node failure from the ground through a wireless connection. Statistics are sent, probably via many hops, to a target
Coverage
that can use it nearby or is linked to various networks using the gateway. The sensor nodes might be sta-
Connectivity
Energy efficient
tionary or changeable. Every node has clear data about the neighborhood or not. Communication over
Relay node WSN over ecological hazards is a key issue. These constraints might impact the performance of the sen-
sors/direction finding protocols and resource utilization; thus, they may show the way to the node crash
situation, i.e., software/hardware breakdown, safety threats, extreme power utilization, etc. It is essential
to examine the collision of breakdown over system performance. In the projected method, relay nodes are
used as secure nodes for the positioned sensor nodes. The projected method is separated into two phases:
the intra segregation stage and inter segregation stage. In the intra segregation stage, the unnecessary
nodes reach nearer to the segregation border, and dispersion slowly enlarges the coverage. The projected
model is examined through simulations and compares the results with existing methods.
Ó 2021 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the International Confer-
ence on Nanoelectronics, Nanophotonics, Nanomaterials, Nanobioscience & Nanotechnology.

1. Introduction may increase the topic of coverage, and installation of a large

number of nodes may result in an ineffective network because of
Wireless Sensor Networks are used to accumulate the physical additional crashes and intrusion.
world data in the form of sensed information based on the require- Sensors can be situated over a huge region. Middle nodes use
ment like pressure, temperature, movement, humidity level, etc. Multi-hop data conveying, and there is no centralized observing
[1,2]. This information is available to the sink through the gateway. node that can make sure the dependable end-to-end conversation
Sensors are installed in large numbers and on account of its wire- [4]. The sensors-based conversation is exposed to collapse, result-
less environment; it simply operates in all atmospheres. Even ing in information loss, QoS derogation, link breaks, network fail-
though sensor nodes are still installed arbitrarily, it’s significant ure, etc. Fault decision and its treatment methods cause extra
to organize them cautiously [3]. Installing a small number of nodes management overhead, which can further corrupt the network
performance [5,6]. Each node has partial broadcast power, and it
covers a precise area only. Node crash in a sensitive area can
⇑ Corresponding author. decrease the overall coverage area, and several node failures may
E-mail address: satya.sp14@gmail.com (P. Satyanarayana). also lead to network segregation.

https://doi.org/10.1016/j.matpr.2021.05.355
2214-7853/Ó 2021 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the International Conference on Nanoelectronics, Nanophotonics, Nanomaterials,
Nanobioscience & Nanotechnology.

Please cite this article as: P. Satyanarayana, T. Mahalakshmi, R .Sivakami et al., A new algorithm for detection of nodes failures and enhancement of net-
work coverage and energy usage in wireless sensor networks, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2021.05.355
P. Satyanarayana, T. Mahalakshmi, R .Sivakami et al. Materials Today: Proceedings xxx (xxxx) xxx

A sensor network [28–30] is nothing but a group of sensor nodes To simulate the entire module with various analysis, and com-
having lightweight, small, and battery-operated devices. In this sen- pare the performance with the existing techniques.
sor network, every node was built with wireless-communication The paper organisation is done as per the following order: Sec-
devices. To monitor some special kind of physical phenomena, var- tion 2 surveys latest algorithms on the fault detection with the var-
ious sensor networks are usually deployed from the deployment ious challenges. Section 3 presents the proposed model in fault
region. To monitor the surrounding region’s temperature or humid- detection and coverage analysis. The simulation results of the pro-
ity, a sensor network may be deployed somewhere [7,8]. posed model are presented in section 4 along with its conclusion.
WSN is a group of nodes configured randomly with no perma-
nent infrastructure. Those networks are usable when infrastructure
is damaged or costly, or not available. These networks show regu- 2. Related work
lar topology modify besides regular host movement.
Mobile Adhoc networks do not need cellular infrastructure. On Xiong, Y., et al. [11] (2019) has placed its focus on improving the
the other hand, it employs multi-hop wireless links where informa- network lifetime by considering the coverage of the energy har-
tion is routed using intermediate nodes. Every node behaves as a vested WSN. A two phase lifetime enhancement module for
receiver and transmitter [11–13]. In most real-world scenarios, improving the overall lifetime of the module. The locations of the
where backbone infrastructure is not required, WSNs are preferred, node were optimally selected through the newly developed multi-
simple to install, and most excellent used where the infrastructure is objective particle swarm optimization algorithm. Even though the
missing or impractical. In many applications mobile Adhoc networks scheme enhanced the lifetime, the proposed model had ignored
are used. Some of the applications are in ear phone, personal area the connectivity and the realistic charging model.
network, laptop, wrist watch and cell phone can contribute in Menaria, V.K., et al. [12] (2020) have presented a node-link fail-
network. ure fault tolerance model (NLFFT Model) for handling the faults
WSN facility can avail in tanks, military environment planes within the network. The model used the minimum spanning tree
and soldiers. WSNs can be used in boats, meeting rooms, small for identifying and replacing the faulty links. The scheme finds
air craft’s, sport stadiums and taxi cab networks. WSNs can also the faulty edges and identifies the appropriate neighbor node for
be used in various crisis operations such as policing, fire fighting, the connection. But, this scheme can be applied to the statistically
search and rescue operations. WSNs are facing so many challenges, deployed sensor environment.
some of them are limitations imposed by mobility, limited Gobinath, T. and Tamilarasi, A., [13] (2020) have introduced a
resources and limitations of wireless networks [16–20]. robust failure node detection module for detecting the failure
Generally there are so many limitations for wireless mobile nodes and connections. A dynamic routing path was determined
Adhoc networks those includes transmit environment, packet fail- through the Lyapunoy optimization technique, and herby
ure, regular partitions, limited bandwidth and changeable capacity improved time and energy. But, the path switching may lead to
links. The major limitations of the mobile Adhoc networks include the high power cost.
small battery life time and restricted capabilities [9,10]. Particu- Masdari, M. and Özdemir, S., [14] (2020) have presented a dis-
larly in WSN, the mobility environment of nodes requires fresh tributed fuzzy logic for the fault node detection in WSN. The algo-
adaptations and applications in flow control and congestion in rithm computes a weight factor based on the distance, coverage,
Transport layer, protocol stack in application layer, handoff and and sensed values for determining and replacing the faulty nodes.
media access in Link layer, routing and addressing in the Network The accuracy of the sensor node detection was small.
layer, and broadcast interference and errors in Physical layer. Sumalatha, M.S. and Nandalal, V., [15] (2020) have developed
Packet delivery ratio (PDR) is defined as the amount of data cross layer security based fuzzy trust calculation mechanism
packets successfully reached to the origin node’s target node. Over- (CLS-FTCM) for the WSN. The security protocol used the fuzzy logic
head in routing is defined as amount of routing information pack- based calculation ffor the overhead monitoring. The fault monitor-
ets produced in the network. The attainment of routing protocol ing was done through the enhanced Convolutional neural network.
decreases as the overhead in routing increases, resulting in the The model had failed to monitor different attacks on the network.
quality of service in wireless mobile Adhoc networks. Delay in data Du (2005) [31] projected and implemented a innovative helpful
transmission in wireless mobile Adhoc networks is defined as the caching scheme for MANET. The method is called ‘‘COOP” it identi-
ratio of time between several packets broadcasted. The number fies sources of information that can give small amount of commu-
of data packets received to the total time needed to reach the tar- nication expenditure. It gets free of caching duplications as
get. To increase the performance of the network, delays must be maximum as possible and enhance efficiency and information
minimized [21–27]. availability. The experiments are prepared in terms of time effi-
The distant nodes obtain statistics starting from a base location ciency, energy efficiency and information availability.
or by using an incident sensed by solitary or supplementary inputs Miranda and Leggio (2005) [32] stressed on reproduction of
to the module with flash memory. Furthermore, the surrounded information in numerous nodes so as to enhance performance.
firmware can be enhanced throughout the wireless network on The algorithm proposed by them was named as PC cache interface
the ground. Protocol stack comprises the transport layer, applica- latency restrictions with probabilistic method for proficient helpful
tion layer, data link layer network layer, physical layer, task man- caching. This method allows a node to have information gathered
agement plane and mobility management plane. Different types of from different node such as one step neighbor in a decentralized
application software can be build and frequently used in applica- manner.
tion layer depending on the various sensing tasks. Kuppusamy and Kalaavathi (2012) [33] concentrated on data
The major contributions of this work towards the fault detec- consistency and data availability problems in Mobile Adhoc net-
tion in the energy aware WSN is defined below: works. To attain the multipurpose, they planned two methods
To develop a fault detection module for creation of the energy named as Adaptive Push and Pull Algorithm for Cluster Based Data
aware WSN model. Consistency (CBDC) and clusters correspondingly. In order to
To identify and improve the coverage area through the inter and accomplish this it follows exact cluster head configuration.
the intra seggreagation phases. Guo and Yang (2005) [34] considered the effect of cache time-
To establish the relay nodes for positioning the sensor nodes in out and backup routes on faithful source routing. The analysis
environment. exposed that in case of heavy mobility situation, the backup routes
2
P. Satyanarayana, T. Mahalakshmi, R .Sivakami et al. Materials Today: Proceedings xxx (xxxx) xxx

have its impact on the toughness to mobility whereas timeout In exactly, the difficulty of increasing the coverage region is
scheme has its importance on performance. given in detail below.
Ma et al. (2010) [7] implemented a supportive cache-based con- Input data assumptions:
tent distribution structure (CCCDF) among two nodes to contain
supportive caching for proficient content delivery. Two strategies  Area ‘D’ with the dimension of P* Q.
are provided such as minimum, maximum and optimal. The earlier  P, Q: the length and width of the 2D area ‘D’ respectively.
takes care of contented deliverance efficiency whereas the last  m: amount of sensor models.
takes care of fairness.  b: quantity of sensors.
 bi: sensor quantity for kind i (i = 1, 2, . . ., m), therefore
3. Proposed work
b =R(i = 0)mbI ð2:7Þ
Let every packet send by a node have a single growing chain
number. Let the receiving point in time of the packet ‘a’ on node
‘b’ with respect to the ideal clock be indicated by pr(a,b) and the  ri: the sensor sensing area is i (i = 1, 2, . . ., m)
transmitting point in time of the packet ‘a’ on node ‘b’ be px (a,
b). The sending or receiving instant is the time immediately prior Output data assumptions:
to the primary byte of a packet is transmitted or received. Imagine The each sensor node position.
that S and R are the source and destination for a lane, correspond- Objective:
ingly. Then, pr(a,S) is the creation time of packet ‘a’ on the basis Increasing the coverage region of ‘b’ sensors on R (denoted coA):
node. The delay of the packet ‘a’ for a lane can be calculated as.
coR = region ([(i = 1)k [(j = 1)b Cr (mi, ni) U A) ð2:8Þ
pd (a) = pr(a, R) —pr(a, S) ð2:1Þ
Let the waiting instant for packet ‘a’ at node ‘b’ on the lane be with cri (mij, nij) ;nij Þ is the circle at (mij, nij), area is ri; the region
tw(a,b) Then, it can be calculated by (M) is the district of the domain M.
Algorithm
pw (a, b) = px (a, b)- pr (a, b) ð2:2Þ
The waiting instance pw (a, b) includes the backoff time on Step 1: Open the network simulator.
node to challenge for the channel. Moreover, the end-to-end delay Step 2: Initialize the network.
is also calculated by Step 3: Select the network size.
Step4: Network with ‘‘n” number of nodes will be displayed.
pd(a)= R(b = 1)(n-1) pw (a,b) ð2:3Þ Step 5: Initiate intra partition phase.
Step 6: Partition the whole network in to group of clusters.
Coverage is nothing but, how well a WSN is covering an area of
(Assume no. of clusters are 4).
attention. The coverage can be separated into three types: region
Step 7: After grouping, identify relay node, redundant node, and
Coverage, direct Coverage and fence Coverage. As the name signi-
common node between two clusters in each group or clusters.
fies region coverage means the how to cover an region of attention.
Step 8: Intra separation stage was finished.
Direct coverage deals with the coverage of points in an area. The
Step 9: Initiate inter partition phase.
main problem of the fence coverage is to discontinue the diffusion
Step 10: Add one free relay node between two groups or
of fence by the intruder. If we deduct the overlapped region from
clusters.
the whole region then we can get the proficient coverage region.
Step 11: Transmit the data packets from origin node to target
The proficient coverage region (Peffective) can be designed as, sup-
node.
pose the whole area of the region of attention is T and the over-
Step 12: Calculate transmission range, coverage improvement
lapped area is Rz then
using equation (2.1) and plot the graph between Transmission
Peffective = T- Rz ð2:4Þ range vs percentage of coverage improvement for different no.
of nodes (n = 50, 150, 250, . . ...).
Efficient coverage area ratio (Ar) will be
Step 13: Calculate sensing range, coverage improvement using
Qr = (T- Rz)/T ð2:5Þ equation (2.2) and plot the graph between sensing range vs per-
centage of coverage improvement for different no. of nodes
Qr = 1- (Rz /T) ð2:6Þ (n = 50, 150, 250, . . ...).

Fig. 3.1. (a): Sensing range vs % Coverage improvement of the sensor nodes. (b): Transmission range vs% Coverage improvement of the sensor nodes.

3
P. Satyanarayana, T. Mahalakshmi, R .Sivakami et al. Materials Today: Proceedings xxx (xxxx) xxx

Fig. 3.2. (a): Results of proposed method when sensor nodes communication ranges are varying. (b): Results obtained for distance travelled when numbers of partitions are
increasing.

Step 14: Calculate the travelled distance using equation (2.3) enhancement in coverage area of the network with C3R and GTA
and plot the graph between number of partitions vs distance are compared with the proposed algorithm shown in the Fig.3.3.
travelled for different number of nodes. (n = 50, 150, 250. . ..). In this trial the network is divided in to ten segregations, the
Step 15: Measure the transmission range and distance travelled sensing range as well as broadcast range and sensing ranges are
using equation (2.4) and also plot the graph between transmis- 90 m and 20 m. The nodes range is varying starting from 20 to
sion range vs. distance travelled for different number of nodes. 140. The results shown in Fig.3.3, the proposed algorithm gives
(n = 50, 150, 250 . . ..). improved performance when compared with the GTA and C3R in
Step 16: Calculate and plot the graph between number of nodes the broken region of the network.
vs. percentage of improvement in coverage for C3R, GTA, pro- In Fig.3.4, shows that, in the proposed approach the nodes
posed algorithms. energy reduces with a growing communication area until a specific
Step 17: Calculate and plot the graph between transmission communication range rA ffi 90 m is obtained. If the sensor nodes
range vs. residual energy for C3R, GTA, proposed algorithms. range increases, the transmission range also increases. As a result,

4. Results

The outcome for fraction enhancement within the coverage

area of entire system by properly adjusting the sensing or broad-
cast range are shown in Fig 3.1a, b. The outcome of adjusting
broadcast range from ‘00 to ‘1400 for different number of nodes
(50, 150, 250 nodes) is given in Fig 3.1b. The results of proposed
algorithm demonstrate that the coverage area of partitions maxi-
mizes, when the broadcast range grows.
The outcome for adjusting sensor range from ‘100 to ‘700 for dif-
ferent number of nodes (50, 150, 250 nodes) is given in Fig 3.1a.
The results of proposed algorithm demonstrate that the coverage
area of the network grows, when the sensing range rises. To rein-
state connectivity among segregations as well as to improve the
coverage area of the network, whole length traverse by the nodes
versus broadcast range and quantity of segregations is demon-
strated in Fig 3.2a–b.
The outcome obtained in Fig 3.1a demonstrates that, the com-
munication area of nodes are increases when the length traversed
by the nodes improves. The larger representative area offers higher
transmission range of the transmit node and great length connec-
tivity between the nodes. Therefore, the nodes be able to travel
extra length surrounded by variety of a transmit node. Fig 3.2a
shows that whole traversed length rises for 50, 150 and 250 nodes.
From Fig 3.2b, it is identified that when the numbers of parti-
tions are increases, the length traversed for every node increases.
The investigational outcomes for proposed algorithm in fraction Fig. 3.3. Coverage analysis comparison with proposed algorithm, C3R and GTA.

4
P. Satyanarayana, T. Mahalakshmi, R .Sivakami et al. Materials Today: Proceedings xxx (xxxx) xxx

References

[1] K. Dasquapta, K. Kalpakis, P. Namjoshi, An efficient clustering-based heuristic

for data gathering and aggregation in sensor networks. In: Proceedings of the
IEEE Wireless Communications and Networking Conference (WCNC 2003),
New Orleans, LA (March 2003).
[2] N. Tamboli, M. Younis, Coverage-aware connectivity restoration in mobile
sensor networks, J. Network Comput. Appl. 33 (3) (2010) 363–374.
[3] A. Howard, M.J. Mataric, G.S. Sukhatme, Mobile sensor network deployment
using potential fields: A distributed scalable solution to the area coverage
problem. In: Proc. 6th Int. Conf. Distributed Autonomous Robotic Syst.,
Fukuoka, Japan, pp. 299–308 (2002).
[4] M. Younis, R. Waknis, ‘‘Connectivity restoration in wireless sensor networks
using Steiner tree approximations,” in Proc. IEEE GLOBECOM, Miami, FL, pp. 1-
5, Dec. 2010.
[5] Mohamed F. Younis, Sookyoung. Lee, and Ameer A Abbasi, ‘‘A localized
algorithm for Restoring Internode Connectivity in Networks of Moveable
Sensors,” in Proc. IEEE trans. On Computers, Vol. 59, No.12, Dec. 2010.
[6] A.A. Abbasi, M.F. Younis, U.A. Baroudi, Recovery from a node failure in wireless
sensoractor networks with minimal topology changes, IEEE Trans. Veh.
Technol. 62 (1) (Jan. 2013).
[7] P. Satyanarayana, V.B.K.L. Aruna, P.N.V.S. Kumar, International boundary
scanning and ship surveillance system, International Conference on
Electrical, Electronics, Communication Computer Technologies and
Optimization Techniques, ICEECCOT 2017, 2018, 2018-January, pp. 212–215.
[8] G. Gupta, M. Younis. ‘‘Fault-tolerant clustering of wireless sensor networks”. In
Proceedings of IEEE WCNC, pages 1579–1584, 2003.
[9] X. Cheng, D.-Z. Du, L. Wang, B.B. Xu, Relay sensor placement in wireless sensor
Fig. 3.4. Total energy left in the network after restoration of network connectivity. networks, IEEE Trans. Comput. (2001).
[10] T.H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein, Introduction to Algorithms,
second ed., MIT Press and McGraw-Hill, 2001.
energy dissipation increases and results in communication over- [11] Y. Xiong, G. Chen, M. Lu, X. Wan, M. Wu, J. She, A two-phase lifetime-
enhancing method for hybrid energy-harvesting wireless sensor network, IEEE
head increases. Sens. J. 20 (4) (2019) 1934–1946.
The result for fraction improvement within the coverage region [12] V.K. Menaria, S.C. Jain, N. Raju, R. Kumari, A. Nayyar, E. Hosain, NLFFT: A novel
of the entire system by properly adjusting the number of nodes is fault tolerance model using artificial intelligence to improve performance in
wireless sensor networks, IEEE Access 8 (2020) 149231–149254.
shown in Fig. 3.3. The outcome of adjusting broadcast range from [13] T. Gobinath, A. Tamilarasi, RFDCAR: Robust failure node detection and
‘00 to ‘500 for different number of nodes (50, 100, 150, 200, 250, dynamic congestion aware routing with network coding technique for
300 nodes) is given in Fig. 3.3. The results of proposed algorithm wireless sensor network, Peer-to-Peer Networking and Applications 13 (6)
(2020) 2053–2064.
demonstrate that the coverage area of partitions maximizes when [14] M. Masdari, S. Özdemir, Towards coverage-aware fuzzy logic-based faulty
the broadcast range grows. node detection in heterogeneous wireless sensor networks, Wireless Pers.
Commun. 111 (1) (2020) 581–610.
[15] M.S. Sumalatha, V. Nandalal, An intelligent cross layer security based fuzzy
5. Conclusion trust calculation mechanism (CLS-FTCM) for securing wireless sensor network
(WSN), J. Ambient Intell. Hum. Comput. (2020) 1–15.
[16] P. Satyanarayana, R. Narmadha, Real time implementation of air quality
This article demonstrates the idea of coverage difficulty in wire- monitoring system using iot and ubidots, Int. J. Recent Technol. Eng. 8 (3)
less sensor networks. The problem of worth of service is extra clo- (2019) 7680–7685.
sely associated to the coverage difficulty in WSNs. The motivation [17] W. Heinzelman, A. Chandrakasan and H. Balakrishnan, ”Energy-Efficient
Communication Protocol for Wireless Microsensor Networks,” Proceedings
for the coverage problem is to contain the monitoring of at most of the 33rd Hawaii International Conference on System Sciences (HICSS ’00),
one node at each point of targeted area. Proficient coverage can January 2000.
be defined as the system life span by relating the characteristics [18] A. Manjeshwar, D.P. Agarwal, ”TEEN: a routing protocol for enhanced
efficiency in wireless sensor networks,” In 1st International Workshop on
like sensing capability and energy utilization by sensing nodes. Parallel and Distributed Computing Issues in Wireless Networks and Mobile
This document has presented a broad outline regarding the cover- Computing, April 2001.
age and connectivity problems of WSNs. In the article, we have to [19] Manjeshwar, D.P. Agarwal, ”APTEEN: A hybrid protocol for efficient routing
and comprehensive information retrieval in wireless sensor networks,”
cover every parameter like formation of nodes if it is random,
Parallel and Distributed Processing Symposium., Proceedings International,
regional or deterministic etc., network description whether it is IPDPS 2002, pp. 195-202.
uniform or varied and type of algorithm, i.e., distributed, localized [20] P. Satyanarayana, R. Narmadha, Implementation of wireless sensor network
based indoor air quality monitoring system using GSM, Int. J. Eng. Adv.
or centralized all that is in relative with connectivity, coverage and
Technol. 8 (6) (2019) 4885–4889.
WSNs. [21] D. Ganesan, R. Govindan, S. Shenker, D. Estrin, Highly-resilient, energy-
efficient multipath routing in wireless sensor networks, ACM SIGMOBILE
Mobile Computing and Communications Review 5 (4) (2001) 1125.
CRediT authorship contribution statement [22] Pallavi S. Katkar, Prof. (Dr.) Vijay R. Ghorpade, ‘‘A Survey on Energy Efficient
Routing Protocol forWireless Sensor Networks”, (IJCSIT) International Journal
of Computer Science and Information Technologies, Vol. 6, 2015.
P. Satyanarayana: Conceptualization, Methodology. : . T. Maha- [23] N.A. Pantazis, S.A. Nikolidakis, D.D. Vergados, Energy-efficient routing
lakshmi: Visualization, Writing - original draft. R .Sivakami: Data protocols in wireless sensor networks: a survey, IEEE Commun. Surv. Tut. 15
curation, Supervision, Validation. Saad Ali Alahmari: Writing - (2) (2013) 551–591.
[24] G.S. Brar, S. Rani, H. Song, S.H. Ahmed, ‘‘Energy Efficient Direction-Based
review & editing. Sivaram Rajeyyagari: Software, Validation. PDORP Routing Protocol for WSN”, IEEE Special Section on Green
Srinivasulu Asadi: Writing - review & editing. Communications and Networking for 5g Wireless, Vol. 4, pp: 3182-3194, 2016.
[25] Gherbi Chirihane, Aliouat Zibouda, ‘‘Distributed energy efficient adaptive
clustering protocol with data gathering for large-scale wireless sensor
Declaration of Competing Interest networks”, Programming and Systems (ISPS), 12th International Conference,
IEEE,2015.
[26] A. Ahmad, K. Latif, N. Javaid, A. Khan, U. Qasim, Density controlled divide-and-
The authors declare that they have no known competing finan-
rule scheme for energy efficient routing in Wireless Sensor Networks,
cial interests or personal relationships that could have appeared Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE
to influence the work reported in this paper. Canadian Conference on, vol., no., pp.1,4, 5-8 May 2013.

5
P. Satyanarayana, T. Mahalakshmi, R .Sivakami et al. Materials Today: Proceedings xxx (xxxx) xxx

[27] Seongsoo Jang, Ho-Yeon Kim, Nam-Uk Kim and TaiMyoung Chung, ‘‘Energy- [31] Y. Du, S.K. Gupta, 2005, October. COOP-A cooperative caching service in
Efficient Clustering scheme with Concentric Hierarchy,” RF and Microwave MANETs. In Joint International Conference on Autonomic and Autonomous
Conference (RFM), 2011 IEEE International, vol., no., pp.79,82, 12-14 Dec. Systems and International Conference on Networking and Services-(icas-isns’
2011. 05) (pp. 58-58). IEEE.
[28] T. Clausen, P. Jacquet, L. Viennot, and G. Hansen, Optimizing route discovery in [32] H. Miranda, S. Leggio, L. Rodrigues, K. Raatikainen, 2005. A stateless
reactive protocols for ad hoc networks, vol. 8, pp. 1–8, 2002. neighbour-aware cooperative caching protocol for ad-hoc networks.
[29] G.R. Pathak, S.H. Patil, A.D. Rana, Y.N. Suralkar, 2014, Mathematical model for [33] P. Kuppusamy, B. Kalaavathi, 2012, July. Cluster based cooperative caching
routing protocol performance in NS2: Comparing DSR, AODV and DSDV as approach through mobility prophecy in MANET. In 2012 Third International
example. In Wireless Computing and Networking (GCWCN), 2014 IEEE Global Conference on Computing, Communication and Networking Technologies
Conference on (pp. 184-188). IEEE. (ICCCNT’12) (pp. 1-5). IEEE.
[30] A. Barve, A. Kini, O. Ekbote, J. Abraham, ‘‘Optimization of DSR routing protocol [34] S. Guo, O. Yang, Y. Shu, Improving source routing reliability in mobile ad hoc
in MANET using passive clustering,’’ in Proc. 2nd Int. Conf. Commun. Control networks, IEEE Trans. Parallel Distrib. Syst. 16 (4) (2005) 362–373.
Intell. Syst. (CCIS), Nov. 2016, pp. 23–27.